After this course, students should be expected to be able to …
Tentative subjects include:
Required: ISL - An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Recommended: ESL - The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman
We will have an R tutorial during the first couple of weeks.
A course which covers linear regression and uses R, such as STAT 425. Good knowledge of probability and linear algebra is also assumed.
R and RStudio are required software for this course. R is a freely available language and environment for statistical computing and graphics. RStudio is a free and open-source integrated development environment for R. You must have access to a computer where you are able to install the most up-to-date versions of R and RStudio, as well as install R packages.
There will be ten homework assignments. The best nine will count towards your grade (lowest score dropped). All homework must be organized using R Markdown. The compiled PDF (or html) file along with the original source code file (.rmd) should be submitted through Compass2g.
There will be two in-class quizzes. The quiz dates are:
Project due dates, assignment details, and group assignments will be announced after the midpoint of the semester.
## Warning: package 'pander' was built under R version 3.5.3| Type | Precentage |
|---|---|
| Homework | 50% |
| Quiz I | 10% |
| Quiz II | 10% |
| Group Choice | 1% |
| Project Proposal | 4% |
| Project Report | 25% |
Letter grades
| A+ | A | A- | B+ | B | B- | C+ | C | C- | D+ | D | D- |
|---|---|---|---|---|---|---|---|---|---|---|---|
| TBD | 93% | 90% | 87% | 83% | 80% | 77% | 73% | 70% | 67% | 63% | 60% |
You are expected to attend all lectures and discussions. Failure to do so may not have a direct effect on your course grade, but will likely have a significant indirect effect. Any known or potential extracurricular conflicts should be discussed in person with the instructor during the first week of classes, or as soon as they arise.
The official University of Illinois policy related to academic integrity can be found in Article 1, Part 4 of the Student Code. Section 1-402 in particular outlines behavior which is considered an infraction of academic integrity. These sections of the Student Code will be upheld in this course. Any violations will be dealt with in a swift, fair and strict manner. Homework assignments are meant to be learning experiences. You may discuss the exercises with other students, but you must write up the solutions on your own. In short, do not cheat, it is not worth the risk. You are more likely to get caught than you believe. If you think you may be operating in a grey area, you most likely are.
The instructor reserves the right to make any changes he considers academically advisable. Such changes, if any, will be announced in class. Please note that it is your responsibility to attend the class and keep track of the proceedings.